In the field of data handling and signal processing a signal is often recorded/registered by a transducer, and the signal is thereafter digitalized by an A/D converter. The recorded and digitalized signal would typically be used for analysis and/or statistical information.
Auscultation is an example of a technique where a transducer is used to record/register a physical phenomenon, and where the signal is used for analysis and/or statistics. Auscultation is the technical term for listening to the Internal sounds of the body. Auscultation is normally performed for the purposes of examining the cardiovascular system and respiratory systems (heart and lung sounds) as well as the gastrointestinal system (bowel sounds). The sounds are often recorded from a patient by a microphone, A/D converted and stored as a digital file for later analysis in order to diagnose the patient.
In many applications only a part of the recorded signal is used in the analysis. A particular part of the signal could namely be used to extract special parameters that could be used to diagnose the patient. For instance, the lung sounds due to respiration comprise a part where the patient breaths in and a part where the patient breaths out. Both parts of the lung sounds could be used to extract parameters with diagnostic value, however, it is not certain that the parameters could be used for the same diagnose, and it is not certain that it would be the same parameters. Another example could be the heart sounds that comprise two major parts; namely a systolic and a diastolic part. Both parts reflect the cardiac cycle, and the systolic part is the part of the cardiac cycle in which the heart muscle contracts, forcing the blood into the main blood vessels, and the diastole is the part of the heart cycle during which the heart muscle relaxes and expands. During diastole, blood fills the chambers. Both parts of the sound could provide clinicians and other medical professionals with parameters that could be used to diagnose the patient.
In many applications it is therefore necessary to divide the signal into segments in order to perform data handling on the particular segments. Typically the signal is divided into segments using a periodic segmentation where the signal is e.g. divided into segments corresponding to a number of samples or a given time period. This method assumes that the recorded signal is 100% periodical; otherwise the different segments would overlap and thus not be separated properly. Another way of dividing a signal into segments is to find special characteristic(s) in the signal and simply divide the signal into segments using these special characteristic(s). However, it can be difficult to find these special characteristic(s) when the signal to noise ratio of the signal is often very bad, and the segmentation would therefore often fail.
Another problem when dividing a signal into segments is the fact that the individual segments might contain a lot of noise and therefore might be unusable for further data handling.
A consequence of the existing segmentation methods is that wrong or noisy segments are often used for further data handling. The consequence of this could be crucial, especially if the segments are used for diagnostic purpose as is the case in auscultation.